Abstract: Aspects of the technology described herein improve an object recognition system by specifying a type of picture that would improve the accuracy of the object recognition system if used to retrain the object recognition system. The technology described herein can take the form of an improvement model that improves an object recognition model by suggesting the types of training images that would improve the object recognition model's performance. For example, the improvement model could suggest that a picture of a person smiling be used to retrain the object recognition system. Once trained, the improvement model can be used to estimate a performance score for an image recognition model given the set characteristics of a set of training of images. The improvement model can then select a feature of an image, which if added to the training set, would cause a meaningful increase in the recognition system's performance.

Abstract: A 3D segmentation editing system accurately updates the segmentations of non-edited images of a 3D scan to reflect segmentation edits applied to other images of the scan using localized interpolation. In one or more embodiments, rather than replacing the entireties of the initial segmentations of non-edited images with newly generated, globally interpolated segmentations, the segmentation editing system applies a distance-based criterion to the interpolation of segmentation edits, such that only portions of the segmentations of the non-edited images that correspond to areas that were manually annotated in the edited images will be modified by the interpolation process, and the initial segmentations will be maintained outside of those edited areas. In this way, the system merges the interpolated segmentation with the initial segmentation for each non-edited image in a manner that mitigates unreliable modifications to the initial segmentations in areas far from the edited areas.

Abstract: A computer-implemented method of perceiving structure in an environment comprises steps of: receiving at least one structure observation input pertaining to the environment; processing the at least one structure observation input in a perception pipeline to compute a perception output; determining one or more uncertainty source inputs pertaining to the structure observation input; and determining for the perception output an associated uncertainty estimate by applying, to the one or more uncertainty source inputs, an uncertainty estimation function learned from statistical analysis of historical perception outputs.

Abstract: An example embodiment includes: a determination unit that, based on an image including an eye of a recognition subject, determines whether or not a colored contact lens is worn; and a matching unit that, when it is determined by the determination unit that the colored contact lens is worn, performs matching of the iris by using a feature amount extracted from a region excluding a predetermined range including an outer circumference of the iris out of a region of the iris included in the image.

Abstract: This disclosure is directed to techniques in which a first user in an environment scans visual indicia associated with an item, such as a barcode, before handing the item to a second user. One or more computing devices may receive an indication of the scan, retrieve image data of the interaction from a camera within the environment, identify the user that received the item, and update a virtual cart associated with the second user to indicate addition of the item.

Abstract: The present disclosure relates to a system that includes one or more processors configured to receive a plurality of aerial property data corresponding to a target property, and applying a height threshold to the plurality of aerial property images. Additionally, the system is configured to remove areas within the plurality of property images that exceed the height threshold, and apply a perimeter calculation to define a perimeter around the target property within the aerial property images. Further, the system calculates an area of the detected vegetation and structures within the perimeter of the aerial property images.

Abstract: An example embodiment includes: a determination unit that, based on an image including an eye of a recognition subject, determines whether or not a colored contact lens is worn; and a matching unit that, when it is determined by the determination unit that the colored contact lens is worn, performs matching of the iris by using a feature amount extracted from a region excluding a predetermined range including an outer circumference of the iris out of a region of the iris included in the image.

Abstract: Disclosed herein are systems and methods for classifying a tympanic membrane by using a classifier. The classifier is a machine learning algorithm. A method for classifying a tympanic membrane includes steps of: receiving, from an interrogation system, one or more datasets relating to the tympanic membrane; determining a set of parameters from the one or more datasets, wherein at least one parameter of the set of parameters is related to a dynamic property or a static position of the tympanic membrane; and outputting a classification of the tympanic membrane based on a classifier model derived from the set of parameters. The classification comprises one or more of a state, a condition, or a mobility metric of the tympanic membrane.

Abstract: A system for detection and identification of objects and obstacles near, between or on railway comprise several forward-looking imagers adapted to cover each different range forward and preferably to be sensitive each to different wavelength of radiation, including visible light, LWIR, and SWIR. The substantially homogeneous temperature along the rail the image of which is included in an imager frame assists in identifying and distinguishing the rail from the background Image processing is applied to define living creature in the image frame and to distinguish from a man-made object based on temperature of the body. Electro optic sensors (e.g. thermal infrared imaging sensor and visible band imaging sensor) are used to survey and monitor railway scenes in real time.

Abstract: Image captured through a non-rectilinear lens may exhibit distortions. The distortions may be reduced by warping the image. However, warping the image may degrade the fidelity of the image. The warped image may be enhanced to increase the fidelity of the image. The enhancement may be applied to the portions of the image that were degraded from the warping.

Abstract: Performing semantic segmentation in an absence of labels for one or more semantic classes is provided. One or more weak predictors are utilized to obtain label proposals of novel classes for an original dataset for which at least a subset of sematic classes are unlabeled classes. The label proposals are merged with ground truth of the original dataset to generate a merged dataset, the ground truth defining labeled classes of portions of the original dataset. A machine learning model is trained using the merged dataset. The machine learning model is utilized for performing semantic segmentation on image data.

Abstract: A deep learning model may be trained to provide an estimated image of the interior of a patient, based on a number of image sets, each image set comprising an interior image of the interior of a person and a contour image of the person's outer contour at a specific point in time. The model is trained to establish an optimized parametrized conversion function G specifying the correlation between the interior of the person and the persons outer contour based on the image sets. The conversion function G can then be used to provide estimated images of patient's interior based on their contours.

Abstract: A non-standard user interface object identification system includes an object candidate extractior that extracts one or more objects from an image, a first similarity analyzer that determines object type candidates of the one or more objects in accordance with similarities between the one or more objects and a standard user interface (UI) element, a second similarity analyzer that selects object type-specific weight values in accordance with layout characteristics of the one or more objects and determines object types of the one or more objects using the object type candidates and the object type-specific weight values, and an object identifier that receives type and characteristic information of a search target object and identifies the search target object in accordance with characteristic information and the object types of the one or more objects.

Abstract: An example embodiment includes: a determination unit that, based on an image including an eye of a recognition subject, determines whether or not a colored contact lens is worn; and a matching unit that, when it is determined by the determination unit that the colored contact lens is worn, performs matching of the iris by using a feature amount extracted from a region excluding a predetermined range including an outer circumference of the iris out of a region of the iris included in the image.

Abstract: Techniques related to optical flow estimation for equirectangular images are discussed. Such techniques include combining an optical flow map generated using an input pair of equirectangular images and an optical flow map generated using a transformed pair of equirectangular images rotated with respect to the input pair to move polar regions in the input pair to central regions in the transformed pair.

Abstract: A computer-implemented method is provided for detecting a dynamic object in a visual line of sight of an aircraft. The method includes implementing, by a computer system, the following operations: receiving at least two image frames; detecting a horizon within at least one of the at least two image frames; segmenting each of the at least two image frames into at least first and second search regions using the detected horizon, wherein the first search region is defined above the detected horizon in the at least two image frames and the second search region is defined below the horizon in the at least two image frames; determining, across the at least two image frames, a motion vector field within at least one of the first search region or the second search region; and identifying a detection proposal using the determined motion vector field.

Abstract: Systems and methods for dynamic sensor data adaptation using a deep learning loop are provided. A method includes classifying, using a discriminator model, a first object from first sensor data associated with a first sensing condition, wherein the discriminator model is trained for a second sensing condition different from the first sensing condition; generating, using a generator model in response to the discriminator model failing to classify the first object, second sensor data representing a second object comprising at least a modified element of the first object; classifying, using the discriminator model, the second object from the second sensor data; and adapting, based at least in part on a difference between the first object and the second object in response to the discriminator model successfully classifying the second object, a machine learning model associated with object classification for the first sensing condition.

Abstract: Methods, systems, and storage media for classifying content across media formats based on weak supervision and cross-modal training are disclosed. The system can maintain a first feature classifier and a second feature classifier that classifies features of content having a first and second media format, respectively. The system can extract a feature space from a content item using the first feature classifier and the second feature classifier. The system can apply a set of content rules to the feature space to determine content metrics. The system can correlate a set of known labelled data to the feature space to construct determinative training data. The system can train a discrimination model using the content item and the determinative training data. The system can classify content using the discrimination model to assign a content policy to the second content item.

Abstract: A method for identifying a user using an image of an object of the user, the method comprising: obtaining, by an optical sensor of a mobile device, the image of the object, wherein the object comprises a biometric characteristic of the user; providing the image to a neural network; processing the image by the neural network to identify-a position of the object and the object in the image; extracting, from the identified object, the biometric characteristic; storing the biometric characteristic in a storage device; and providing at least the biometric characteristic as input to an identification means to determine whether the biometric characteristic identifies the user.

Abstract: An information processing system (10) includes: an acquisition unit (50) configured to sequentially acquire a plurality of elements included in sequential data; a first calculation unit (110) configured to calculate, for each of the plurality of elements, a first indicator indicating which one of a plurality of classes the element belongs to; a weight calculation unit (130) configured to calculate, for each of the plurality of elements, a weight according to a confidence related to calculation of the first indicator; a second calculation unit (120) configured to calculate, based on the first indicators each weighted with the weight, a second indicator indicating which one of the plurality of classes the sequential data belongs to; and a classification unit (60) configured to classify the sequential data as any one of the plurality of classes, based on the second indicator. According to such an information processing system, sequential data can be appropriately classified.